Electronics, Vol. 12, Pages 1338: Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding

Electronics, Vol. 12, Pages 1338: Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding

Electronics doi: 10.3390/electronics12061338

Authors: Ibrahim Taabane Daniel Menard Anass Mansouri Ali Ahaitouf

The newest video compression standard, Versatile Video Coding (VVC), was finalized in July 2020 by the Joint Video Experts Team (JVET). Its main goal is to reduce the bitrate by 50% over its predecessor video coding standard, the High Efficiency Video Coding (HEVC). Due to the new advanced tools and features included in VVC, it actually provides high coding performances—for instance, the Quad Tree with nested Multi-Type Tree (QTMTT) involved in the partitioning block. Furthermore, VVC introduces various techniques that allow for superior performance compared to HEVC, but with an increase in the computational complexity. To tackle this complexity, a fast Coding Unit partition algorithm based on machine learning for the intra configuration in VVC is proposed in this work. The proposed algorithm is formed by five binary Light Gradient Boosting Machine (LightGBM) classifiers, which can directly predict the most probable split mode for each coding unit without passing through the exhaustive process known as Rate Distortion Optimization (RDO). These LightGBM classifiers were offline trained on a large dataset; then, they were embedded on the optimized implementation of VVC known as VVenC. The results of our experiment show that our proposed approach has good trade-offs in terms of time-saving and coding efficiency. Depending on the preset chosen, our approach achieves an average time savings of 30.21% to 82.46% compared to the VVenC encoder anchor, and a Bjøntegaard Delta Bitrate (BDBR) increase of 0.67% to 3.01%, respectively.